In the storage and reproduction of images from an original document, or other kinds of image data (e.g. visual), and more particularly to the storage and rendering of image data representing an original document that has been electronically scanned, storage efficiencies and high reconstructive image quality can be better realized if the image data is segmented in a manner to better facilitate the storage and rendering. One such segmenting method comprises identifying an image or image region as continuous tone (contone) or halftone (clustered halftone). In addition, estimating the frequency ranges of the halftones (low or high) is also advantageous for better storage and reproduction efficiencies. Accurate segmentation by such identification facilitates mixed raster content (“MRC”) modeling useful for achieving high compression ratios while maintaining high reconstructed image quality. Reliable halftone detection is also important for avoiding moiré artifacts.
By “contone” is meant continuous tone images that use different concentration colorants such as cyan, magenta and yellow to produce different colors. The term “continuous” comes from the fact that, at each spatial location, such printing varies the color and concentrations or amounts over a continuous range. Contone printers require reliable and accurate spatial control of colorant concentrations, which is difficult to achieve and control accurately. As a result, contone printers are rather expensive. Most desktop printers are therefore based on the simpler technique of halftoning. Halftoning exploits the spatial low pass characteristics of the human eye. Color halftone images are produced by placing a large number of small, differently colored dots on paper. Due to the low pass nature of the eye's spatial response, the effective spectrum seen by the eye is the average of the spectra over a small angular subtense. Different colors are produced by varying the relative areas of the differently colored dots. In contrast with contone printing, the concentration of a colorant within a dot is not varied, so halftone printers are considerably easier and less expense to manufacture.
Halftone imaging detection is an important procedure for many applications. Halftone imaging can be identified for an entire page, or in some instances for an image region thereon. Halftone detection usually not only classifies an image into contone or halftone, but also estimates the frequency ranges of the halftones. Typically the frequency ranges are lumped into two, high frequency and low frequency. In object oriented rendering, the image regions with different kinds of halftone textures are treated differently in enhancement, color conversion, and halftoning to achieve optimized image quality. Similarly, in MRC segmentation, different kinds of regions may be coded in different manners for different halftone features, for optimizing data storage and processing efficiencies.
Accurate halftone detection is not easy. Quite often, complicated operations are required. (c.f. U.S. Pat. No. 6,185,328, the disclosure of which is incorporated herein by reference in its entirety.)
Accordingly, there is a need for a halftone detection method which minimizes complicated operational steps and can be implemented with relatively simple calculations, while maintaining accurate characteristic classification.
Halftone noises are high-pass in nature. By “halftone noise” is meant the noise introduced during the halftoning process. By “high-pass” is meant that the high frequency is dominant in data. The halftone noise frequency is generally higher in frequency than the image content frequency. However, this is not always true, particularly in the regions of an image where sharp edges exist. As a strong edge contains a spectrum rich in high frequencies, the halftone image and image content may not be separable in frequency domain in the vicinity of the edges. Halftones of different frequencies have different halftoning noise energy distributions. The high frequency halftone noise has a higher cutoff frequency than the low frequency one.
Accordingly, there is a need for a method and system which can classify images, or image regions, into contone, high frequency halftone or low frequency halftone using noise energy distribution to facilitate better image segmentation, more efficient data storage, and all while maintaining high reconstructed image quality.